Learning multiple graphs for document recommendations

Ding Zhou, Shenghuo Zhu, Kai Yu, Xiaodan Song, Belle L. Tseng, Hongyuan Zha, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

99 Scopus citations

Abstract

The Web offers rich relational data with different semantics. In this paper, we address the problem of document recommendation in a digital library, where the documents in question are networked by citations and are associated with other entities by various relations. Due to the sparsity of a single graph and noise in graph construction, we propose a new method for combining multiple graphs to measure document similarities, where different factorization strategies are used based on the nature of different graphs. In particular, the new method seeks a single low-dimensional embedding of documents that captures their relative similarities in a latent space. Based on the obtained embedding, a new recommendation framework is developed using semi-supervised learning on graphs. In addition, we address the scalability issue and propose an incremental algorithm. The new incremental method significantly improves the efficiency by calculating the embedding for new incoming documents only. The new batch and incremental methods are evaluated on two real world datasets prepared from Cite Seer. Experiments demonstrate significant quality improvement for our batch method and significant efficiency improvement with tolerable quality loss for our incremental method.

Original languageEnglish (US)
Title of host publicationProceeding of the 17th International Conference on World Wide Web 2008, WWW'08
Pages141-150
Number of pages10
DOIs
StatePublished - Dec 15 2008
Event17th International Conference on World Wide Web 2008, WWW'08 - Beijing, China
Duration: Apr 21 2008Apr 25 2008

Publication series

NameProceeding of the 17th International Conference on World Wide Web 2008, WWW'08

Other

Other17th International Conference on World Wide Web 2008, WWW'08
CountryChina
CityBeijing
Period4/21/084/25/08

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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